Field of the Invention
The invention disclosed here relates in general to the field of medical diagnostics, and more specifically methods for noninvasively diagnosing or predicting the risk of systemic medical conditions.
Description of the Related Art
Shock is the systemic pathophysiologic state characterized by organ perfusion inadequate to the tissues metabolic needs, and the organismal physiologic and pathophysiologic responses to this inadequate perfusion. At the tissue level, inadequate perfusion results in insufficient delivery of oxygen and metabolic substrate, and insufficient removal of waste products, including carbon dioxide.
The measurement of a patient's physical, chemical and anatomical properties is a central component of medical diagnosis. It has been appreciated by others that electromagnetic radiation may be directed into the body, and then transmitted or reflected energy used in medical diagnosis. The use of roentgen rays (i.e., electromagnetic radiation in the x-ray wavelengths) for production of diagnostic images is particularly well known. The body itself creates measurable electromagnetic fields, which are measured clinically by devices such as the electrocardiogram and electroencephalogram. Also known is the transmission, absorption, and/or reflectance of near-infrared and infrared wavelengths into tissues for the measurement of various molecular species and the state of cells and tissues, such as the use of near-infrared spectroscopy to measure oxygen saturation of hemoglobin. (Jobsis 1264-67)
When subjected to inadequate blood flow, tissues undergo a number of changes that may be detectable non-invasively using optical technologies. Among these are: decreased oxygen tension, increased carbon dioxide tension, decreased pH, altered energy metabolism, altered redox potential within the cytochrome. Additionally, there are alterations in the hemogram and hemodynamics that may be detectable, including the heart rate, hematocrit and red blood cell velocities. Some of the various molecular species that constitute the metabolome may also be measureable, in particular, the concentration of glucose.
Using electromagnetic potential or impedance it is also possible to measure, or more likely approximate, a number of clinically important measures of hemodynamics. Among these are cardiac output and ventricular stroke.
Importantly, it is likely that the measurement of tissue and organ parameters non-invasively has significant inaccuracy and the validity of the derived measurements in the setting of disease and hemodynamic instability may be limited. (Lewis et al. 1334-38) Such inaccuracy may underlie the limited adoption of single measurement non-invasive technologies clinically.
Previous to this disclosure, medical diagnostics based on physical measurements have generally been limited to the production of images, or the uniplex measurement of optical or physical properties of tissues or organs. Previously, optical measurements, such as tissue oxygen status have only been measured at single location on or within the body. The electrocardiogram has been traditionally measured at multiple locations, but these have been presented to clinicians only as a series of uniplex vectors.
Previous to this disclosure, it has not been appreciated that clinically useful diagnostic biomarkers for shock might be synthesized from multiple noninvasive optical measurements.
The present disclosure is for a system intended generally to predict the risk of, or assist in the diagnosis of, shock. Shock is the complex systemic pathophysiologic state generally characterized by organ perfusion inadequate to the tissues metabolic needs, and the organismal physiologic and pathophysiologic responses to this inadequate perfusion. Shock may be caused by any pathological process that interferes with hemodynamics, including hemorrhage, sepsis, or myocardial infarction. Depending on the degree of insult, and the adequacy of physiologic response, shock may be transient and reversible. If, however, the degree of insult is greater than the compensatory capacity of the organism, shock may become an unstable state that progresses to hemodynamic collapse and death.
Traditionally, biomarkers diagnostic or predictive of shock were pre-existing measured hemodynamic parameters that were used as surrogate indicators. In particular, these have included blood pressure, time for capillary refill, among others. These have been measured directly via catheters, or indirectly via acoustics. Regardless, the diagnostic inadequacy of traditional measurements is well-documented. (Cohn 118-22)
The limited utility of traditional measurements has led to the measurement of alternative indicators of hemodynamics, including cardiac output, ventricular stroke volume, and systemic vascular resistance. Again, these may be measured directly via catheters or indirectly via techniques such as impedance. However, similar to the traditional measurements of blood pressure, utilization of additional hemodynamic parameters has not provided an adequate solution to the diagnosis of shock.
The continuing need for earlier detection of shock is universally acknowledged by clinicians to be an important unmet need. In an e-mail from Baghdad dated Jun. 20, 2007 military physicians stated “there are three groups of casualties: 1) the ones who are really sick and (almost) everyone knows it; 2) the ones who have minimal injuries and will live almost regardless of what we do; 3) those who look like they aren't too bad but then deteriorate. (We are) most interested in identifying Group 3.”
The poor performance of traditional markers has led to attempts to identify innovative tissue sensors with improved performance. Single biosensors for measurement of skin, tissue and visceral organ status utilizing existing technology such as pulse oximetry, near-infrared reflectance, Doppler flow have been extensively studied as possible early non-invasive detectors of shock.
Of note, all attempts to develop innovative tissue sensors have focused on the optimizing the measurement in one location. (Soller et al. 475-81) The possibility that improved diagnostic performance might be achieved by multiplex measurement of tissue parameters at multiple anatomic locations has not been previously considered.
Depending on the degree of insult, and the host physiologic response, the measurable tissue and organ parameters vary predictably as the shock state develops. The body attempts to spare vital organs, in particular the brain and heart, limiting early effects to non-vital organs such as the skin and digestive viscera. Measurements of tissue status will reflect these patterns in various organ. Physiologically distinct organ systems include: the central nervous system, the heart and arterial vasculature, the venous vasculature, the kidneys, skeletal musculature, the mucosa, the dermis and epidermis, among others. Even among these specific systems, shock may have variable anatomic effects. For instance, the effect on the dermis and epidermis may be different between the distal extremities, the trunk, and the head. The same would likely be true with respect to the skeletal musculature.
Although uniplex measurement of tissue parameters at single locations has proven inadequate in the development of diagnostic methods for shock, it may be appreciated that a great deal more information is available in the anatomic and temporal patterns of tissue measurements.
The present disclosure is for a system intended principally to predict the risk of, or assist in the diagnosis of the systemic disease of shock. As envisioned, multiple sensors are placed at physiologically distinct locations, and machine learning is utilized to derive algorithms that may combine optical, electromagnetic, anatomical and temporal inputs, among others, to create a synthetic biomarker that outperforms any single measurement clinically.
Recently, machine learning derived multiplex algorithms constructed from the measurement of multiple individual serum molecular concentrations have been widely studied as innovative in vitro diagnostics. (Kato 248-51) These same approaches, however, have not been applied in systemic disease to non-molecular measurements such as those based on electromagnetic or optical sensing.
As in molecular multivariate assays, it is widely appreciated that useful mathematical diagnostic algorithms may be developed using the in silico techniques variously called machine learning, data mining, and big data, among other terms. For the purposes of the present disclosure, the term “machine learning” will be used to represent all possible mathematical in silico techniques for creation of useful algorithms from large data sets. The term “algorithm” will be utilized in reference to the clinically useful mathematical equations or computer programs produced by the process disclosed. Particularly important to the present disclosure is the widely acknowledged phenomena that the performance of machine learning derived algorithms is independent of the specific in silico software routine used for its derivation. If the same training data set is used, techniques as different as supervised learning, unsupervised learning, association rule learning, hierarchical clustering, multiple linear and logistic regressions are likely to produce algorithms whose clinical performance is indistinguishable.
Although the techniques of machine learning are to a great extent interchangeable, it is well known to those skilled in the art that the independence of the individual variables used in the model is of great importance. Multiple variables will bring no additional diagnostic performance if they are highly correlated and essentially measure the same tissue parameter. With respect to the present invention, it is anticipated that the utilization of anatomic and temporal patterns of organ systems that are physiologically distinct in their response to impending shock will enhance the performance of the algorithm.
Any diagnostic method initially developed to diagnose disease may also be used to guide therapy. With respect to the present invention, the algorithm may also be optimized as an adjunct to resuscitation and treatment of shock. As such, it would function as a goal for directing therapy. Such targeted therapeutics are often called theranostics.
These and other objects, features and advantages of the present invention will become clearer when the drawings as well as the detailed description are taken into consideration.
Prior Art
There is no prior art teaching the use of temporal and anatomic patterns derived from the noninvasive measurement of tissue status in multiple physiologically distinct locations in a method for diagnosing or predicting the risk of shock.
The following comprehensive searches of the world wide web find no results: “multiple optical sensors for diagnosis of shock”, “multiple electromagnetic sensors for diagnosis of shock”, “multiple optical sensors for diagnostic of shock”, “multiple electronic sensors for diagnostic of shock”, “multiple optical sensors for prediction of shock”, “multiple electromagnetic sensors for prediction of shock”, “multiplex optical system for diagnosis of shock”, “multiplex optical system for prediction of shock”, “anatomic patterns in shock”, “anatomic and temporal patterns in shock”,
A search for “a combination of electromagnetic and optical sensors” resulted in no citations within the life sciences.
Comprehensive Pubmed searches reveal the following: a title search combining “multiple” and “optical” and “shock” provides no results; a title search combining “multiple” and “optical” and “shock” provides 1 result which was unrelated to the subject matter of the present invention; a title search combining “multiplex” and “optical” provides 20 results, none of which teach a method remotely similar to the presently disclosed invention.
When utilization of multiple electromagnetic or optical sensors has been taught previously, the intent was directed not at systemic illness or shock, but local pathophysiologic events such as vascular occlusion or local tissue ischemia. In such proposed devices, the placement pattern of the sensors empirically reflects known anatomic structures, and the information derived is anatomically local in nature. For instance, the specific leads in the standard electrocardiogram are intended to indicate normality or injury in the area of the myocardium represented by specific vectors. Boyden et al (US2009/0281413A1) utilizes multiple optical sensors and statistical learning with the intention of identifying a vascular occlusive event. The optical arrays are proximal and distal to the possible occlusion and reflect known local vascular anatomy in a straightforward and deterministic manner.
Summary of Deficiencies in the Prior Art: 1) No non-invasive diagnostic methods for shock incorporate multiplex sensing; 2) No non-invasive diagnostic methods for shock incorporate multiplex optical sensing; 3) No non-invasive diagnostic methods for shock incorporate multiplex electromagnetic sensing; 4) No non-invasive diagnostic methods for shock incorporate multiplex sensing that combines optical and electromagnetic sensing; 5) No non-invasive diagnostic methods for shock incorporate multiplex sensing based on an optimized algorithm; 6) No non-invasive diagnostic methods for shock that algorithmically incorporate sensing in multiple physiologically distinct anatomic location; 7) No non-invasive diagnostic methods for shock that algorithmically incorporate temporal patterns; 8) No non-invasive diagnostic methods for shock that algorithmically incorporate combination of anatomic and temporal patterns; 8) No non-invasive diagnostic methods for shock that algorithmically incorporate combination of anatomic and temporal patterns with traditional measurements such as the electrocardiogram.